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plot_svhn.py
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plot_svhn.py
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import numpy as np
import matplotlib.pyplot as plt
import sys, os
from lib.config import cfg
if sys.version_info[0] == 2:
import cPickle as pickle
else:
import pickle
def plot_accuracies(data, title='Accuracy Plot', plot_type='Accuracy', x_axis_label='Epochs', save_location=None, mode='Test', size=None):
"""
This helper function can be used to plot(visualize) the accuracies saved using lib.utils.save_accuracies()
:return: None
"""
plt.tick_params(direction='out', length=6, width=2)
if(save_location==None):
save_location = './final_plots/svhn/'
if not os.path.exists(save_location):
os.makedirs(save_location)
for (i,info) in enumerate(data):
with open(info[0], 'rb') as f:
acc = pickle.load(f)
_plot_indiv_accuracies(acc, color=info[2], label=info[1], plot_type=plot_type)
if(info[1]=='SGD'):
with open(info[3], 'rb') as f:
upper_limit = pickle.load(f)
with open(info[4], 'rb') as f:
lower_limit = pickle.load(f)
x = np.arange(1, len(acc) + 1)
if(plot_type=="Accuracy"):
lower_limit = 100-lower_limit
upper_limit = 100-upper_limit
plt.fill_between(x, lower_limit, upper_limit, color='lightskyblue')
if(size==None):
size = 15
plt.legend(fontsize=size)
# if title is not None:
# plt.title(title)
plt.xlabel(x_axis_label,fontsize=size)
plt.grid(True, linestyle='--', axis='y')
if plot_type == 'Accuracy':
plt.yticks(np.arange(0, 15, step=2))
if(mode=='Test'):
plt.ylabel('Test Error',fontsize=size)
elif(mode=='Train'):
plt.ylabel('Train Error',fontsize=size)
plt.ylim([3,15])
else:
# plt.yticks(np.arange(0, 2, step=0.5))
if (mode == 'Test'):
plt.ylabel('Test Loss',fontsize=size)
elif (mode == 'Train'):
plt.ylabel('Train Loss',fontsize=size)
plt.ylim([0.1, 0.5])
plt.savefig(save_location +"/"+ title.replace(' ', '_').replace('(', '_').replace(')', '_') + '.eps', format='eps')
plt.close()
def _plot_indiv_accuracies(accuracies, color='blue', label='', plot_type=None, linestyle='-'):
x = np.arange(1, len(accuracies) + 1)
if(plot_type=='Accuracy'):
accuracies = 100 - np.array(accuracies)
plt.plot(x, accuracies, color=color, label=label, linestyle=linestyle)
def plot_ablations_accuracies(data, title='Accuracy Plot', plot_type='Accuracy', x_axis_label='Epochs', save_location=None, mode='Test'):
"""
This helper function can be used to plot(visualize) the accuracies saved using lib.utils.save_accuracies()
:return: None
"""
plt.tick_params(direction='out', length=6, width=2)
if(save_location==None):
save_location = './final_plots/svhn/'
if not os.path.exists(save_location):
os.makedirs(save_location)
for (i,info) in enumerate(data):
with open(info[0], 'rb') as f:
acc = pickle.load(f)
if(info[1][0:3]=='SGD'):
# with open(info[3], 'rb') as f:
# upper_limit = pickle.load(f)
# with open(info[4], 'rb') as f:
# lower_limit = pickle.load(f)
# x = np.arange(1, len(acc) + 1)
# if(plot_type=="Accuracy"):
# lower_limit = 100-lower_limit
# upper_limit = 100-upper_limit
# plt.fill_between(x, lower_limit, upper_limit, color='lightskyblue')
_plot_indiv_accuracies(acc, color=info[2], label=info[1], plot_type=plot_type, linestyle='--')
else:
_plot_indiv_accuracies(acc, color=info[2], label=info[1], plot_type=plot_type)
size = 15
plt.legend(fontsize=size)
# if title is not None:
# plt.title(title)
plt.xlabel(x_axis_label,fontsize=size)
plt.grid(True, linestyle='--', axis='y')
if plot_type == 'Accuracy':
plt.yticks(np.arange(0, 15, step=2))
if(mode=='Test'):
plt.ylabel('Test Error',fontsize=size)
elif(mode=='Train'):
plt.ylabel('Train Error',fontsize=size)
plt.ylim([3,15])
else:
# plt.yticks(np.arange(0, 2, step=0.5))
if (mode == 'Test'):
plt.ylabel('Test Loss',fontsize=size)
elif (mode == 'Train'):
plt.ylabel('Train Loss',fontsize=size)
plt.ylim([0.1, 0.5])
plt.savefig(save_location +"/"+ title.replace(' ', '_').replace('(', '_').replace(')', '_') + '.eps', format='eps')
plt.close()
if __name__ == '__main__':
#
# for i in range(1, 61):
# try:
# test_data = [[
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_CIFAR_ResNet20_0211_152357/accuracies/test_acc_between_iteration_epoch_' + str(i) + '_accuracy.pkl',
# 'SGD', 'blue'],
# [
# '/home/vamshi/PycharmProjects/SMDL/output/cifar100_resnet32_submodcomb_refresh-5_epochs-60_0203_103925/accuracies/test_acc_between_iteration_epoch_' + str(i) + '_accuracy.pkl',
# 'Submodular Selection', 'green']
# ]
# plot_accuracies(test_data, title='CIFAR100 Epoch ' + str(i) + ' Test Accuracy', x_axis_label='# of iterations (x10)')
# except Exception as error:
# print ('Exception occured for index {}, {}'.format(i, error))
###################################################################################################################################
# # SVHN
# Main Result
test_data = [
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/test_acc_mean_accuracy.pkl',
'SGD', 'blue', '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/test_acc_upper_limit_accuracy.pkl',
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/test_acc_lower_limit_accuracy.pkl'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_LOSS_SVHN_ResNet20_0215_192444/accuracies/test_acc_round_0_accuracy.pkl',
'LOSS', 'darkviolet'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_0211_154707/accuracies/test_acc_round_0_accuracy.pkl',
'SMDL', 'green']
]
plot_accuracies(test_data, title='SVHN Test Error (Main)', mode='Test')
train_data = [[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/train_acc_round_0_accuracy.pkl',
'SGD', 'blue', '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/train_acc_upper_limit_accuracy.pkl',
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/train_acc_lower_limit_accuracy.pkl' ],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_LOSS_SVHN_ResNet20_0215_192444/accuracies/train_acc_round_0_accuracy.pkl',
'LOSS', 'darkviolet'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_0211_154707/accuracies/train_acc_round_0_accuracy.pkl',
'SMDL', 'green']
]
plot_accuracies(train_data, title='SVHN Train Error (Main)', mode='Train')
train_loss_data = [[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/train_loss_mean_accuracy.pkl',
'SGD', 'blue', '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/train_loss_upper_limit_accuracy.pkl',
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/train_loss_lower_limit_accuracy.pkl'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_LOSS_SVHN_ResNet20_0215_192444/accuracies/train_loss_round_0_accuracy.pkl',
'LOSS', 'darkviolet'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_0211_154707/accuracies/train_loss_round_0_accuracy.pkl',
'SMDL', 'green']
]
plot_accuracies(train_loss_data, title='SVHN Train Loss (Main)', plot_type='Loss', mode='Train')
test_loss_data = [[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/test_loss_mean_accuracy.pkl',
'SGD', 'blue', '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/test_loss_upper_limit_accuracy.pkl',
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/test_loss_lower_limit_accuracy.pkl'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_LOSS_SVHN_ResNet20_0215_192444/accuracies/test_loss_round_0_accuracy.pkl',
'LOSS', 'darkviolet'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_0211_154707/accuracies/test_loss_round_0_accuracy.pkl',
'SMDL', 'green']
]
plot_accuracies(test_loss_data, title='SVHN Test Loss (Main)', plot_type='Loss', mode='Test')
##################################################################################################
#SVHN Ablations
##################### refresh rate ###########################
test_data = [[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/test_acc_mean_accuracy.pkl',
'SGD', 'blue', '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/test_acc_upper_limit_accuracy.pkl',
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/test_acc_lower_limit_accuracy.pkl'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_0211_154707/accuracies/test_acc_round_0_accuracy.pkl',
'SMDL Refresh Rate-5', 'green'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_Refresh10_0212_101539/accuracies/test_acc_round_0_accuracy.pkl',
'SMDL Refresh Rate-10', 'violet'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_Refresh25_0212_101854/accuracies/test_acc_round_0_accuracy.pkl',
'SMDL Refresh Rate-25', 'navy'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_refresh40_0221_223031/accuracies/test_acc_round_0_accuracy.pkl',
'SMDL Refresh Rate-40', 'brown'],
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_Refresh50_0212_102202/accuracies/test_acc_round_0_accuracy.pkl',
# 'SMDL Refresh Rate-50', 'black']
]
plot_accuracies(test_data, title='SVHN Test Error (RF)', save_location='./final_plots/svhn/Refresh')
train_data = [[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/train_acc_round_0_accuracy.pkl',
'SGD', 'blue', '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/train_acc_upper_limit_accuracy.pkl',
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/train_acc_lower_limit_accuracy.pkl'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_0211_154707/accuracies/train_acc_round_0_accuracy.pkl',
'SMDL Refresh Rate-5', 'green'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_Refresh10_0212_101539/accuracies/train_acc_round_0_accuracy.pkl',
'SMDL Refresh Rate-10', 'violet'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_Refresh25_0212_101854/accuracies/train_acc_round_0_accuracy.pkl',
'SMDL Refresh Rate-25', 'navy'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_refresh40_0221_223031/accuracies/train_acc_round_0_accuracy.pkl',
'SMDL Refresh Rate-40', 'brown'],
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_Refresh50_0212_102202/accuracies/train_acc_round_0_accuracy.pkl',
# 'SMDL Refresh Rate-50', 'black']
]
plot_accuracies(train_data, title='SVHN Train Accuracy (RF)', save_location='./final_plots/svhn/Refresh')
train_loss_data = [[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/train_loss_mean_accuracy.pkl',
'SGD', 'blue', '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/train_loss_upper_limit_accuracy.pkl',
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/train_loss_lower_limit_accuracy.pkl'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_0211_154707/accuracies/train_loss_round_0_accuracy.pkl',
'SMDL Refresh Rate-5', 'green'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_Refresh10_0212_101539/accuracies/train_loss_round_0_accuracy.pkl',
'SMDL Refresh Rate-10', 'violet'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_Refresh25_0212_101854/accuracies/train_loss_round_0_accuracy.pkl',
'SMDL Refresh Rate-25', 'navy'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_refresh40_0221_223031/accuracies/train_loss_round_0_accuracy.pkl',
'SMDL Refresh Rate-40', 'brown'],
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_Refresh50_0212_102202/accuracies/train_loss_round_0_accuracy.pkl',
# 'SMDL Refresh Rate-50', 'black'],
]
plot_accuracies(train_loss_data, title='SVHN Train Loss (RF)', plot_type='Train Loss', save_location='./final_plots/svhn/Refresh')
test_loss_data = [[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/test_loss_mean_accuracy.pkl',
'SGD', 'blue', '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/test_loss_upper_limit_accuracy.pkl',
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/test_loss_lower_limit_accuracy.pkl'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_0211_154707/accuracies/test_loss_round_0_accuracy.pkl',
'SMDL Refresh Rate-5', 'green'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_Refresh10_0212_101539/accuracies/test_loss_round_0_accuracy.pkl',
'SMDL Refresh Rate-10', 'violet'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_Refresh25_0212_101854/accuracies/test_loss_round_0_accuracy.pkl',
'SMDL Refresh Rate-25', 'navy'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_refresh40_0221_223031/accuracies/test_loss_round_0_accuracy.pkl',
'SMDL Refresh Rate-40', 'brown'],
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_Refresh50_0212_102202/accuracies/test_loss_round_0_accuracy.pkl',
# 'SMDL Refresh Rate-50', 'black'],
]
plot_accuracies(test_loss_data, title='SVHN Test Loss (RF)', plot_type='Test Loss', save_location='./final_plots/svhn/Refresh')
################### batch_size ###########################
test_data = [
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/test_acc_mean_accuracy.pkl',
'SGD batch size-50', 'blue'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_batch100_0221_013806/accuracies/test_acc_round_0_accuracy.pkl',
'SGD batch size-100', 'green'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_batch200_0220_184912/accuracies/test_acc_round_0_accuracy.pkl',
'SGD batch size-200', 'violet'],
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_batch500_0220_185242/accuracies/test_acc_round_0_accuracy.pkl',
# 'SGD batch size-500', 'violet'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_0211_154707/accuracies/test_acc_round_0_accuracy.pkl',
'SMDL batch size-50', 'navy'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_batch100_0215_225602/accuracies/test_acc_round_0_accuracy.pkl',
'SMDL batch size-100', 'darkgreen'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_batch200_0215_225815/accuracies/test_acc_round_0_accuracy.pkl',
'SMDL batch size-200', 'darkviolet'],
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_batch500_0215_230014/accuracies/test_acc_round_0_accuracy.pkl',
# 'SMDL batch size-500', 'mediumvioletred']
]
plot_ablations_accuracies(test_data, title='SVHN Test Error (BS)', save_location='./final_plots/svhn/Batch')
train_data = [
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/train_acc_round_0_accuracy.pkl',
'SGD batch size-50', 'blue'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_batch100_0221_013806/accuracies/train_acc_round_0_accuracy.pkl',
'SGD batch size-100', 'green'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_batch200_0220_184912/accuracies/train_acc_round_0_accuracy.pkl',
'SGD batch size-200', 'violet'],
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_batch500_0220_185242/accuracies/train_acc_round_0_accuracy.pkl',
# 'SGD batch size-500', 'blue'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_0211_154707/accuracies/train_acc_round_0_accuracy.pkl',
'SMDL batch size-50', 'navy'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_batch100_0215_225602/accuracies/train_acc_round_0_accuracy.pkl',
'SMDL batch size-100', 'darkgreen'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_batch200_0215_225815/accuracies/train_acc_round_0_accuracy.pkl',
'SMDL batch size-200', 'darkviolet'],
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_batch500_0215_230014/accuracies/train_acc_round_0_accuracy.pkl',
# 'SMDL batch size-500', 'black']
]
plot_ablations_accuracies(train_data, title='SVHN Train Error (BS)', save_location='./final_plots/svhn/Batch')
test_loss_data = [
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/test_loss_mean_accuracy.pkl',
'SGD batch size-50', 'blue'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_batch100_0221_013806/accuracies/test_loss_round_0_accuracy.pkl',
'SGD batch size-100', 'green'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_batch200_0220_184912/accuracies/test_loss_round_0_accuracy.pkl',
'SGD batch size-200', 'violet'],
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_batch500_0220_185242/accuracies/test_loss_round_0_accuracy.pkl',
# 'SGD batch size-500', 'blue'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_0211_154707/accuracies/test_loss_round_0_accuracy.pkl',
'SMDL batch size-50', 'navy'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_batch100_0215_225602/accuracies/test_loss_round_0_accuracy.pkl',
'SMDL batch size-100', 'darkgreen'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_batch200_0215_225815/accuracies/test_loss_round_0_accuracy.pkl',
'SMDL batch size-200', 'darkviolet'],
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_batch500_0215_230014/accuracies/test_loss_round_0_accuracy.pkl',
# 'SMDL batch size-500', 'black'],
]
plot_ablations_accuracies(test_loss_data, title='SVHN Test Loss (BS)', plot_type='Loss', save_location='./final_plots/svhn/Batch')
train_loss_data = [
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/train_loss_mean_accuracy.pkl',
'SGD batch size-50', 'blue'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_batch100_0221_013806/accuracies/train_loss_round_0_accuracy.pkl',
'SGD batch size-100', 'green'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_batch200_0220_184912/accuracies/train_loss_round_0_accuracy.pkl',
'SGD batch size-200', 'violet'],
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_batch500_0220_185242/accuracies/train_loss_round_0_accuracy.pkl',
# 'SGD batch size-500', 'blue'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_0211_154707/accuracies/train_loss_round_0_accuracy.pkl',
'SMDL batch size-50', 'navy'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_batch100_0215_225602/accuracies/train_loss_round_0_accuracy.pkl',
'SMDL batch size-100', 'darkgreen'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_batch200_0215_225815/accuracies/train_loss_round_0_accuracy.pkl',
'SMDL batch size-200', 'darkviolet'],
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_batch500_0215_230014/accuracies/train_loss_round_0_accuracy.pkl',
# 'SMDL batch size-500', 'black'],
]
plot_ablations_accuracies(train_loss_data, title='SVHN Train Loss (BS)', plot_type='Loss', save_location='./final_plots/svhn/Batch')
######################################## learning rate #######################################
test_data = [
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/test_acc_mean_accuracy.pkl',
'SGD lr-0.1', 'blue'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_lr0.01_0220_184031/accuracies/test_acc_round_0_accuracy.pkl',
'SGD lr-0.01', 'green'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_lr0.001_0220_184246/accuracies/test_acc_round_0_accuracy.pkl',
'SGD lr-0.001', 'violet'],
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_lr0.0001_0222_010051/accuracies/test_acc_round_0_accuracy.pkl',
# 'SGD lr-0.0001', 'brown'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_0211_154707/accuracies/test_acc_round_0_accuracy.pkl',
'SMDL lr-0.1', 'navy'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_lr0.01_0215_230546/accuracies/test_acc_round_0_accuracy.pkl',
'SMDL lr-0.01', 'darkgreen'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_lr0.001_0215_230720/accuracies/test_acc_round_0_accuracy.pkl',
'SMDL lr-0.001', 'darkviolet'],
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_lr0.0001_0215_230847/accuracies/test_acc_round_0_accuracy.pkl',
# 'SMDL lr-0.0001', 'black']
]
plot_ablations_accuracies(test_data, title='SVHN Test Error (LR)', save_location='./final_plots/svhn/LR')
train_data = [
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/train_acc_round_0_accuracy.pkl',
'SGD lr-0.1', 'blue'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_lr0.01_0220_184031/accuracies/train_acc_round_0_accuracy.pkl',
'SGD lr-0.01', 'green'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_lr0.001_0220_184246/accuracies/train_acc_round_0_accuracy.pkl',
'SGD lr-0.001', 'violet'],
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_lr0.0001_0222_010051/accuracies/train_acc_round_0_accuracy.pkl',
# 'SGD lr-0.0001', 'black'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_0211_154707/accuracies/train_acc_round_0_accuracy.pkl',
'SMDL lr-0.1', 'navy'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_lr0.01_0215_230546/accuracies/train_acc_round_0_accuracy.pkl',
'SMDL lr-0.01', 'darkgreen'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_lr0.001_0215_230720/accuracies/train_acc_round_0_accuracy.pkl',
'SMDL lr-0.001', 'darkviolet'],
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_lr0.0001_0215_230847/accuracies/train_acc_round_0_accuracy.pkl',
# 'SMDL lr-0.0001', 'black']
]
plot_ablations_accuracies(train_data, title='SVHN Train Error (LR)', save_location='./final_plots/svhn/LR')
test_loss_data = [
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/test_loss_mean_accuracy.pkl',
'SGD lr-0.1', 'blue'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_lr0.01_0220_184031/accuracies/test_loss_round_0_accuracy.pkl',
'SGD lr-0.01', 'green'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_lr0.001_0220_184246/accuracies/test_loss_round_0_accuracy.pkl',
'SGD lr-0.001', 'violet'],
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_lr0.0001_0222_010051/accuracies/test_loss_round_0_accuracy.pkl',
# 'SGD lr-0.0001', 'brown'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_0211_154707/accuracies/test_loss_round_0_accuracy.pkl',
'SMDL lr-0.1', 'navy'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_lr0.01_0215_230546/accuracies/test_loss_round_0_accuracy.pkl',
'SMDL lr-0.01', 'darkgreen'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_lr0.001_0215_230720/accuracies/test_loss_round_0_accuracy.pkl',
'SMDL lr-0.001', 'darkviolet'],
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_lr0.0001_0215_230847/accuracies/test_loss_round_0_accuracy.pkl',
# 'SMDL lr-0.0001', 'black'],
]
plot_ablations_accuracies(test_loss_data, title='SVHN Test Loss (LR)', plot_type='Loss',save_location='./final_plots/svhn/LR')
train_loss_data = [
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/train_loss_mean_accuracy.pkl',
'SGD lr-0.1', 'blue'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_lr0.01_0220_184031/accuracies/train_loss_round_0_accuracy.pkl',
'SGD lr-0.01', 'green'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_lr0.001_0220_184246/accuracies/train_loss_round_0_accuracy.pkl',
'SGD lr-0.001', 'violet'],
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/train_loss_round_0_accuracy.pkl',
# 'SGD lr-0.0001', 'blue'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_0211_154707/accuracies/train_loss_round_0_accuracy.pkl',
'SMDL lr-0.1', 'navy'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_lr0.01_0215_230546/accuracies/train_loss_round_0_accuracy.pkl',
'SMDL lr-0.01', 'darkgreen'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_lr0.001_0215_230720/accuracies/train_loss_round_0_accuracy.pkl',
'SMDL lr-0.001', 'darkviolet'],
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_lr0.0001_0215_230847/accuracies/train_loss_round_0_accuracy.pkl',
# 'SMDL lr-0.0001', 'black'],
]
plot_ablations_accuracies(train_loss_data, title='SVHN Train Loss (LR)', plot_type='Loss',save_location='./final_plots/svhn/LR')
################################################ alphas #################################################
test_data = [[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/test_acc_mean_accuracy.pkl',
'SGD', 'blue', '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/test_acc_upper_limit_accuracy.pkl',
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/test_acc_lower_limit_accuracy.pkl'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_apha1_0215_194413/accuracies/test_acc_round_0_accuracy.pkl',
'SMDL-1', 'green'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_apha2_0215_194627/accuracies/test_acc_round_0_accuracy.pkl',
'SMDL-2', 'brown'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_apha3_0215_194850/accuracies/test_acc_round_0_accuracy.pkl',
'SMDL-3', 'violet'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_apha4_0215_195102/accuracies/test_acc_round_0_accuracy.pkl',
'SMDL-4', 'navy'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_0211_154707/accuracies/test_acc_round_0_accuracy.pkl',
'SMDL', 'red'],
]
plot_accuracies(test_data, title='SVHN Test Error (alphas)',save_location='./final_plots/svhn/alphas')
train_data = [[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/train_acc_round_0_accuracy.pkl',
'SGD', 'blue', '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/train_acc_upper_limit_accuracy.pkl',
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/train_acc_lower_limit_accuracy.pkl'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_apha1_0215_194413/accuracies/train_acc_round_0_accuracy.pkl',
'SMDL-1', 'green'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_apha2_0215_194627/accuracies/train_acc_round_0_accuracy.pkl',
'SMDL-2', 'brown'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_apha3_0215_194850/accuracies/train_acc_round_0_accuracy.pkl',
'SMDL-3', 'violet'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_apha4_0215_195102/accuracies/train_acc_round_0_accuracy.pkl',
'SMDL-4', 'navy'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_0211_154707/accuracies/train_acc_round_0_accuracy.pkl',
'SMDL', 'red'],
]
plot_accuracies(train_data, title='SVHN Train Error (alphas)',save_location='./final_plots/svhn/alphas')
test_loss_data = [[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/test_loss_mean_accuracy.pkl',
'SGD', 'blue', '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/test_loss_upper_limit_accuracy.pkl',
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/test_loss_lower_limit_accuracy.pkl'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_apha1_0215_194413/accuracies/test_loss_round_0_accuracy.pkl',
'SMDL-1', 'green'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_apha2_0215_194627/accuracies/test_loss_round_0_accuracy.pkl',
'SMDL-2', 'brown'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_apha3_0215_194850/accuracies/test_loss_round_0_accuracy.pkl',
'SMDL-3', 'violet'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_apha4_0215_195102/accuracies/test_loss_round_0_accuracy.pkl',
'SMDL-4', 'navy'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_0211_154707/accuracies/test_loss_round_0_accuracy.pkl',
'SMDL', 'red'],
]
plot_accuracies(test_loss_data, title='SVHN Test Loss (alphas)', plot_type='Loss',save_location='./final_plots/svhn/alphas')
train_loss_data = [[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/train_loss_mean_accuracy.pkl',
'SGD', 'blue', '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/train_loss_upper_limit_accuracy.pkl',
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/train_loss_lower_limit_accuracy.pkl'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_apha1_0215_194413/accuracies/train_loss_round_0_accuracy.pkl',
'SMDL-1', 'green'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_apha2_0215_194627/accuracies/train_loss_round_0_accuracy.pkl',
'SMDK-2', 'brown'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_apha3_0215_194850/accuracies/train_loss_round_0_accuracy.pkl',
'SMDL-3', 'violet'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_apha4_0215_195102/accuracies/train_loss_round_0_accuracy.pkl',
'SMDL-4', 'navy'],
[
'/home/vamshi/PycharmProjects/SMDL/final_Results/final_SMDL_SVHN_ResNet20_0211_154707/accuracies/train_loss_round_0_accuracy.pkl',
'SMDL', 'red'],
]
plot_accuracies(train_loss_data, title='SVHN Train Loss (alphas)', plot_type='Loss', save_location='./final_plots/svhn/alphas', mode='train')
################# distance metrics ###########################
test_data = [
#[
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/test_acc_mean_accuracy.pkl',
# 'SGD', 'blue', '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/test_acc_upper_limit_accuracy.pkl',
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/test_acc_lower_limit_accuracy.pkl'],
[
'/home/vamshi/PycharmProjects/SMDL/output/svhn_resnet20_submodcomb_euclidean_0131_103658/accuracies/test_acc_round_0_accuracy.pkl',
'euclidean', 'navy'],
[
'/home/vamshi/PycharmProjects/SMDL/output/svhn_resnet20_submodcomb_gaussian_0131_102949/accuracies/test_acc_round_0_accuracy.pkl',
'gaussian', 'darkviolet'],
[
'/home/vamshi/PycharmProjects/SMDL/output/svhn_resnet20_submodcomb_cosine_0131_103759/accuracies/test_acc_round_0_accuracy.pkl',
'cosine', 'blue'],
[
'/home/vamshi/PycharmProjects/SMDL/output/svhn_resnet20_submodcomb_correlation_0131_101252/accuracies/test_acc_round_0_accuracy.pkl',
'correlation', 'green']
]
plot_accuracies(test_data, title='SVHN Test Error (Distance Metrics)',save_location='./final_plots/svhn/distance_metrics')
train_data = [
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/train_acc_round_0_accuracy.pkl',
# 'SGD', 'blue', '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/train_acc_upper_limit_accuracy.pkl',
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/train_acc_lower_limit_accuracy.pkl'],
[
'/home/vamshi/PycharmProjects/SMDL/output/svhn_resnet20_submodcomb_euclidean_0131_103658/accuracies/train_acc_round_0_accuracy.pkl',
'euclidean', 'navy'],
[
'/home/vamshi/PycharmProjects/SMDL/output/svhn_resnet20_submodcomb_gaussian_0131_102949/accuracies/train_acc_round_0_accuracy.pkl',
'gaussian', 'darkviolet'],
[
'/home/vamshi/PycharmProjects/SMDL/output/svhn_resnet20_submodcomb_cosine_0131_103759/accuracies/train_acc_round_0_accuracy.pkl',
'cosine', 'blue'],
[
'/home/vamshi/PycharmProjects/SMDL/output/svhn_resnet20_submodcomb_correlation_0131_101252/accuracies/train_acc_round_0_accuracy.pkl',
'correlation', 'green']
]
plot_accuracies(train_data, title='SVHN Train Error (Distance Metrics)',save_location='./final_plots/svhn/distance_metrics')
test_loss_data = [
# [
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/test_loss_mean_accuracy.pkl',
# 'SGD', 'blue', '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/test_loss_upper_limit_accuracy.pkl',
# '/home/vamshi/PycharmProjects/SMDL/final_Results/final_SGD_SVHN_ResNet20_0211_151827/accuracies/test_loss_lower_limit_accuracy.pkl'],
[
'/home/vamshi/PycharmProjects/SMDL/output/svhn_resnet20_submodcomb_euclidean_0131_103658/accuracies/loss_round_0_accuracy.pkl',
'euclidean', 'navy'],
[
'/home/vamshi/PycharmProjects/SMDL/output/svhn_resnet20_submodcomb_gaussian_0131_102949/accuracies/loss_round_0_accuracy.pkl',
'gaussian', 'darkviolet'],
[
'/home/vamshi/PycharmProjects/SMDL/output/svhn_resnet20_submodcomb_cosine_0131_103759/accuracies/loss_round_0_accuracy.pkl',
'cosine', 'blue'],
[
'/home/vamshi/PycharmProjects/SMDL/output/svhn_resnet20_submodcomb_correlation_0131_101252/accuracies/loss_round_0_accuracy.pkl',
'correlation', 'green'],
]
plot_accuracies(test_loss_data, title='SVHN Test Loss (Distance Metrics)', plot_type='Loss',save_location='./final_plots/svhn/distance_metrics')